气象学报2026，Vol.84Issue(4)：678-693,16.DOI:10.11676/qxxb2026.20250186
S2S模式对2023年夏季华北极端高温的次季节预测技巧及其可预报性来源
Subseasonal prediction skills of ECMWF and CMA S2S models for the extreme high-temperature event over North China in summer 2023 and the sources of predictability
摘要
Abstract
During June—July 2023,North China(NC)experienced a record-breaking Extreme High Temperature(EHT)event with temperatures exceeding 40℃at many meteorological stations.This event consisted of three relatively distinct processes,occurring during 14-18 June(P1),21-25 June(P2),and 30 June—3 July(P3),respectively.Using hindcast data from the European Centre for Medium-Range Weather Forecasts(ECMWF)and the China Meteorological Administration(CMA)within the Subseasonal-to-Seasonal(S2S)Prediction Project and diagnostic analysis of the local temperature budget,surface energy budget,and large-scale atmospheric circulation,this study evaluates the performance of ECMWF and CMA dynamic models in forecasting the spatial distribution and intensity of the three EHT processes,and further reveals the sources of forecast errors and predictability.The results indicate that:(1)ECMWF and CMA models can predict the spatial distribution of Surface Air Temperature(SAT)anomalies over NC for P1,P2 and P3 with lead time of 10-11,12-14,and 3-6 d,respectively.However,both models underestimate the amplitude of SAT anomalies,especially during P3;(2)when the forecast lead time exceeds 15 d for P1 and P2,and 5 d for P3,both models fail to capture features of the mid-to high-latitude Rossby wave train over Eurasia,resulting in prediction biases in both the location and intensity of localized high-pressure anomalies over NC;(3)prediction biases of the localized high-pressure anomalies over NC further induce biases in the forecast of local physical processes and SAT anomalies.During P1 and P3,the models'underestimation of both shortwave and longwave radiation leads to local negative temperature tendency biases,with diabatic heating being the primary contributor.In contrast,during P2,the underestimation of adiabatic heating is primarily responsible for the cold bias in predicted SAT.This study highlights the mid-to high-latitude subseasonal Rossby wave train teleconnection as a critical source of predictability for subseasonal EHT in NC,suggesting that better representation of this wave pattern is a key to improve EHT prediction skills over NC.关键词
华北极端高温/次季节预测/中高纬度罗斯贝波列Key words
Extreme high temperature over North China/Subseasonal prediction/Mid-to high-latitude Rossby wave train分类
天文与地球科学引用本文复制引用
袁可芯,李娟,黄红洁,胡娅敏,朱志伟..S2S模式对2023年夏季华北极端高温的次季节预测技巧及其可预报性来源[J].气象学报,2026,84(4):678-693,16.基金项目
国家自然科学基金气象联合基金项目(U2342208)、甘肃省自然科学基金项目(24JRRA720). （U2342208）
